This demo showcases DistWalk, an open-source distributed workload emulator designed to study the end-To-end latency implications of Linux-based systems. DistWalk is capable of deploying sequences of compute, network, and storage operations arranged within graph-like topologies, to be carried out across multiple servers. It supports a variety of communication protocols and traffic patterns, and enables the customization of several factors, such as the duration and parallelism of compute-intensive operations, the network security and connection handling strategy, and the I/O data access and synchronization mode, among others. DistWalk can be used to experiment with a variety of deployment models for distributed workloads, from bare-metal to virtualized or containerized environments, e.g., using Cloud/Edge infrastructures, OpenStack, Kubernetes, or other orchestrators. This allows Cloud/Edge researchers and developers to perform experimental comparisons of the latency achievable by distributed workload patterns across a wide range of system-level configurations.

Demo: Emulating Distributed Workloads with DistWalk

Andreoli, Remo
;
Burlon, Tommaso;Napolitano, Antonio;Cucinotta, Tommaso
2025-01-01

Abstract

This demo showcases DistWalk, an open-source distributed workload emulator designed to study the end-To-end latency implications of Linux-based systems. DistWalk is capable of deploying sequences of compute, network, and storage operations arranged within graph-like topologies, to be carried out across multiple servers. It supports a variety of communication protocols and traffic patterns, and enables the customization of several factors, such as the duration and parallelism of compute-intensive operations, the network security and connection handling strategy, and the I/O data access and synchronization mode, among others. DistWalk can be used to experiment with a variety of deployment models for distributed workloads, from bare-metal to virtualized or containerized environments, e.g., using Cloud/Edge infrastructures, OpenStack, Kubernetes, or other orchestrators. This allows Cloud/Edge researchers and developers to perform experimental comparisons of the latency achievable by distributed workload patterns across a wide range of system-level configurations.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11382/585912
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